Unveiling the odor representation in the inner brain of Drosophila through compressed sensing

Abstract

The olfaction begins when odorant molecules stimulate a finite number of receptor types in the olfactory neurons. According to the recent connectomics data of the olfactory system, the olfactory neural circuit is multilayered and there is an overall dimensionality reduction from the input to output layer. Compressed sensing (CS) is an algorithm that enables the recovery of high-dimensional signals from the data compressed in a lower dimension when the representation of such signals is sufficiently sparse. By analyzing the recent connectomics data, we find that the organization of the olfactory system effectively satisfies the necessary conditions for CS to work. The neural activity profile of projection neurons (PNs) can be faithfully recovered from a low-dimensional response profile of mushroom body output neurons (MBONs), which can be reconstructed using the electrophysiological recordings of a wide range of odorants. By leveraging the residuals calculated between the measured and the predicted MBON responses, we visualize the perceptual odor space using the residual spectrum and discuss the differentiability of an odor from others. Our study highlights the sparse coding of odor to the receptor space as an essential component for odor identifiability, clarifying the principles underlying the concentration-dependent odor percept. Further, simultaneous exposure of the olfactory system to many different odorants saturates the neural activity profile of PNs, significantly degrading the capacity of signal recovery and resulting in a perceptual state analogous to “olfactory white.” Our study applying CS to the real connectomics data encompassing the neural circuitry from PNs to MBONs via Kenyon cells provides quantitative insights into the neural signal processing and odor representation in the inner brain of . Published by the American Physical Society 2024